Automated impairment detection system and method

ABSTRACT

Systems and methods to determine if an individual is impaired. The system includes a display and a stimulus on the display. The system include a controller that is programmed to move the stimulus about the display and one or more sensors that track eye movements and pupil size of a user due to movement of the stimulus or light conditions. The system includes a processor programmed to analyze the eye movements and pupil data size. The method includes using a testing apparatus and collecting data from the testing apparatus. The method includes storing the collected data. The method includes processing the data with an automated impairment decision engine to determine whether a test subject is impaired. The method may include using machine learning models or statistical analysis to determine whether a test subject is impaired. The automated impairment decision engine may be trained using machine learning and/or statistical analysis.

RELATED APPLICATION

This application claims the benefit of priority under 35 U.S.C. § 119 toU.S. Provisional Patent Application Ser. No. 63/303,604 entitled“AUTOMATED IMPAIRMENT DETECTION SYSTEM AND METHOD” filed on Jan. 27,2022, which is incorporated herein in its entirety.

FIELD OF THE DISCLOSURE

This disclosure is generally related to an automated way to detectactive (real-time) impairment from cannabis and other drugs, or abnormalmental conditions like fatigue through the utilization of gaze vectordata, pupil size data, and optionally other biometric data, evaluated bymachine learning algorithms and optionally other statistical methods.

BACKGROUND

There is presently no automated system which can quickly and accuratelydetermine active impairment from a broad variety of drugs, legal andillicit, including alcohol, cannabis, central nervous systemdepressants, central nervous system stimulants, opiates, and more.Further, there is an acute need to detect and quantify mental stateslike fatigue as a way to facilitate safe driving and working conditions.Cannabis and other drugs are being rapidly legalized in the UnitedStates. This has created the problem of an increased need to understandcannabis and other drug impairment due to increased utilization. Instates where cannabis or other drugs are legal and because the compoundslinger in a user's body, it is now insufficient to determine the simplepresence in the body of impairing molecules (likedelta-9-tetrahydrocannabinol, or tetrahydrocannabinol (THC), incannabis) to determine if a crime, like driving under the influence(DUI), has been committed.

In order to allow for rapid field identification of active impairment ona variety of substances, the human conducted Standardized Field SobrietyTests (SFST) are commonly used. Originally researched and developed from1975 to 1981 by the National Highway Traffic and Safety Administration(NHTSA), the Standardized Field Sobriety Tests are intended to allow apolice officer with no tools other than their observations to determineif a subject is impaired. These tests have been repeatedly validated asreliable measurements of impairment in alcohol, cannabis, and some otherdrugs. They are based on the scientific understanding of how alcohol,cannabis and other drugs impact a user's ability to conduct basic testsof balance and dexterity, and most importantly, how these substancesimpact involuntary eye movement. When properly administered, theStandardized Field Sobriety Tests give officers “probable cause” toarrest a vehicle operator for driving under the influence or drivingwhile impaired. The Drug Recognition Expert (DRE) program expands on theSFSTs for a total of 12 testing steps. DRE officers are the best trainedlaw enforcement officers at determining drug impairment. They go to aspecial school where they learn to perform these tests and interpret theresults. Drug Recognition Experts are the only mechanism currentlyavailable that can detect active cannabis impairment, as well as activeimpairment from drugs other than alcohol.

Although the SFST and Drug Recognition Expert battery tests have beenproven accurate at establishing whether or not a vehicle operator isunder the influence of an intoxicating substance, Drug RecognitionExpert officers and other officers who conduct field sobriety tests aresubject to inescapable opportunities for inaccuracy. Those include:human error in conducting or interpreting tests, subjectivity ofinterpretation in test results, errors due to adverse testingconditions, and a distinct lack of corroborating evidence generated inthe process to validate an officer's determination. This leads to theresults of the tests being routinely called into question by defenseattorneys during the course of a DUI trial.

Despite intensive and challenging training, the opportunity for humanerror is omnipresent due to the precision that properly conducting thetests requires and the reliance on memorized test procedures. Inparticular, the tests that deal with eye movement are multi-part andrely on providing a stimulus of appropriate distinction, size, distance,speed, and angle from the subject's eyes. It is therefore exceedinglydifficult to conduct the tests in a precisely standardized fashion everytime they are administered, and even more difficult to simultaneouslyaccurately interpret the resulting eye movement behavior. To allow humanerror in a process with such important and impactful ramifications isunacceptable.

Compounding these challenges is the distinct lack of objective datagenerated in the process. The only current output of the test is simplythe notes taken by the administering officer, and perhaps a body-cameraor dash-camera video recording, if they were activated. Unfortunately,these videos are not commonly of sufficient quality or steadiness toobserve eye movement, which is a fundamental component of theStandardized Field Sobriety Tests and Drug Recognition Expertevaluation. Other drawbacks and issues also exist.

In view of the foregoing, a need exists for an improved impairmentidentification system and method for police officers in an effort toovercome the aforementioned obstacles and deficiencies of conventionalhuman-conducted impairment testing systems.

SUMMARY

One embodiment of the present disclosure is a system. The systemincludes a display and a stimulus on the display. The system include acontroller that is programmed to move the stimulus about the display.The controller may be programmed to control light conditions for theuser. The system includes one or more sensors that track eye movementsand pupil size of a user due to movement of the stimulus or lightconditions. The system includes a processor programmed to analyze theeye movements and pupil data size.

The controller may be programmed to move the stimulus to perform animpairment test and the processor may be programmed to determine andevaluate impairment based on data from the one or more sensors. Thecontroller may be programmed to stimulate pupil response using varyinglight conditions to perform an impairment test and the processor may beprogrammed to determine and evaluate impairment based on data from theone or more sensors. The one or more sensors may capture pupil size dataand the processor may be programmed to analyze the pupil size data toevaluate impairment.

The one or more sensors may capture gaze vector data of the user'svision and the processor may be programmed to analyze the captured gazevector data to evaluate impairment. The display may be a virtual realityheadset, television, monitor, kiosk-mounted display, augmented realityglasses, a holographic display, or the like. The processor may beprogrammed to utilize statistical models, machine learning, artificialintelligence algorithms, or a combination of these to analyze the eyemovements or other biometric data. The controller may be programmed toprecisely calibrate the system to a face shape, eye characteristics, andeye geometry of the user. The controller may be programmed to move thestimulus smoothly to the left and right, up and down, or in any othermotion.

The controller may be programmed to move the stimulus one or more timesto a horizontal periphery of the user's vision. The controller may beprogrammed to move the stimulus left or right and stop the movement at45 degrees from center. The controller may be programmed to move thestimulus to a vertical periphery of the user's vision. The controllermay be programmed to move the stimulus in a circle or to stimulateconvergence in focus by bringing the stimulus toward the subject's nose.The controller may be programmed to display specific light levels andmeasure pupillary reflex response. The controller may be programmed tocapture data using skin-contact or non-contact sensors, such astemperature, heart rate, heart rate variability, respiratory rate, pulseoxygenation, heart rhythm, blood pressure, and muscle tone. Thecontroller may be programmed to perform chemical analysis on the sweatexcreted by the test subject. The controller may be programmed tomeasure lack of convergence, saccades, nystagmus, hippus, eyesmoothness, reaction time, pupillary rebound dilation, and pupillaryreflex for the purposes of impairment detection.

One embodiment of the disclosure is a method of using a testingapparatus and collecting data from the testing apparatus. The methodincludes storing the collected data. The method includes processing thedata with an automated impairment decision engine to determine whether atest subject is impaired. The method may include using machine learningmodels or statistical analysis to determine whether a test subject isimpaired.

One embodiment of the disclosure is a method of capturing data fromparticipants in both an impaired state and an unimpaired state. Themethod includes applying captured data to unsupervised machine learning.The method includes applying captured data to supervised machinelearning. The method includes applying captured data to statisticalanalysis. The method includes implementing the results of theunsupervised machine learning, supervised machine learning, andstatistical analysis to create an automated impairment decision engineto determine whether a test subject is impaired or unimpaired.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of an embodiment of a method of the presentdisclosure.

FIG. 2 shows a schematic of an embodiment of a system of the presentdisclosure.

FIG. 3 is a flow chart of an embodiment of a method of the presentdisclosure.

While the disclosure is susceptible to various modifications andalternative forms, specific embodiments have been shown by way ofexample in the drawings and will be described in detail herein. However,it should be understood that the disclosure is not intended to belimited to the particular forms disclosed. Rather, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope of the invention as defined by the appended claims.

DETAILED DESCRIPTION

The disclosure includes systems and methods that radically improve uponthe shortcomings of human conducted Standardized Field Sobriety Testingand Drug Recognition Expert testing methods through the use of uniquetechnology that acts upon the same and new indicators of impairment inthe human body as do these human-performed tests. In an embodiment, thesystem is functionally an automated way to detect active (real-time)impairment from cannabis and other drugs, fatigue, or other conditionsthrough the utilization of gaze vector data, pupil size data, andoptionally other biometric data, evaluated by machine learningalgorithms and/or statistical methods. By measuring the physiologicalsigns and symptoms of impairment through a device that runs fullyautomated tests, a significant improvement in identification of activesingle or poly-drug impairment, fatigue, or other conditions isachieved. These tests include those designed to elicit eye movement,measurement of pupil size and rate of dilation, measurement of pulserate, evaluation of blink characteristics, quantification of body sweatvolume or composition, measurement of body temperature, bloodoxygenation, heart rate, heart rate variability, heart rhythm, bloodpressure, respiratory rate, pulse oxygenation, muscle tone and otherbiometric characteristics which may be correlated with impairment orsobriety.

An embodiment of the system uses a head-mounted apparatus or afixture-mounted apparatus with a digital screen or projection on which astimulus and/or varying levels of light conditions can be displayed,such as a virtual reality headset or an augmented reality headset. Thisstimulus shall be tracked by the test subject's eyes in order to performthe tests. The apparatus further includes one or more eye trackingsensors or cameras, and optionally, other biometric sensors for thepurpose of capturing biometric data as discussed above. Those sensorsmay include pulse sensors, moisture sensors, pupil size sensors,temperature sensors, or other biometric data capture sensors such asskin-contact sensors.

The apparatus is designed for the test subject to place their face on ornear while the test is conducted. A fixture-mounted apparatus may bemost useful in situations in which a high rate of test subjectthroughput can be expected. Rather than being affixed to the testsubject's head, the fixture mounted variant requires test subjects toplace their head in a particular location or position so the apparatusmay conduct the tests of eye movement, and so the other optional sensorsmay capture data.

The tests are conducted by controlling the light levels that enter theeyes, and/or displaying a stimulus (typically a colored dot or ball orthe like) on the apparatus' digital screen. The stimulus isautomatically moved according to the programmed testing parameters, orother dynamically adjusted testing parameters, and the test subject isinstructed to follow the stimulus with their eyes. The system mayinclude a controller, or the like, may be used to move the stimulusabout the digital screen in various patterns and/or directions asdiscussed herein. Through the use of eye tracking sensors and/or camerasin the apparatus, the system tracks and captures the test subject's eyemovement video and/or still imagery, eye gaze vector data, pupil sizemeasurements, and may collect other biometric data as the test runsautomatically. The eye video, eye positional coordinates, gaze vectors,and pupil size of the eyes are precisely computed and stored forevaluation by the system. Storage of the resulting data may occurlocally, on removable or non-removable media, or on a cloud server, orother remote storage location. Other optional biometric data such as,but not limited to, temperature, moisture, and pulse sensors can also becaptured and stored for evaluation of the test subject.

The evaluative statistical and/or machine learning/artificialintelligence algorithms are trained using a dataset of impaired andsober individuals whose impairment characteristics are known. Thisproduces algorithms that detect eye movement and pupil sizecharacteristics that can be correlated with precise levels ofimpairment. Utilizing this technique, an extremely high degree ofaccuracy in determining sobriety, impairment, or a variety of othermedical conditions can thus be obtained. This machine learning algorithmmay be trained to act more quickly and with greater accuracy andobjectivity than any human. The use of this automated testing technologyrepresents a significant advance in the detection of impairment from oneor multiple substances, fatigue, and other conditions.

In order to ensure testing accuracy, the frame rates of the apparatus'digital display, the eye tracking sensors and other biometric sensorsmay be synced. By eliminating small variations between frame rates ordata capture rates on eye tracking sensors, cameras, and other biometricsensors, the most precise measurements are obtained. The statistical,machine learning/artificial intelligence algorithms can also be trainedto compensate for variability in sensor data capture rates.

Upon use of the testing apparatus, the system may use any collecteddata, such as, but not limited to eye tracking, pupil size, corneal, eyemeasurements, or the like, to automatically identify the test subject.This may be useful in cases where repeated testing or regular testing isrequired (for example, construction sites or other workplaces). Examplesof data which may be utilized to automatically identify a test subjectincludes eye measurement data such as interpupillary distance, or anyother data captured by the system, such as eye movement characteristics,corneal print, image recognition, pupil size, or other biometric data.The system may utilize this test data to automatically match the testsubject against a database of known test subjects. This test subjectmatching algorithm can then be utilized to group a test subject's testdata together, to send test results to specific third parties, or toallow the system to administer certain tests which may be most relevantto that user.

The test subject matching algorithm can be trained by the system byconducting one or multiple tests which capture the required biometricdata. This data is then characterized and stored in a database. When atest subject begins a test, their biometric data can then be accessedand the test subject matched against the database. Test subjects may beadded or removed by a test administrator or other third party. If a testsubject is found to not be in the database, a test may still beconducted, and the results sent to any third party as required.

While the test apparatus conducts the automatically performed tests, theapparatus collects gaze vector data, pupil size measurement data, andother optional biometric data. This data is then automatically evaluatedby a processor, or the like, programmed to use statistical models,and/or machine learning or artificial intelligence algorithms toidentify characteristics within the data that are consistent withimpairment and/or sobriety. The data processing may take place on thetesting apparatus, on another electronic device such as a mobile phoneor tablet or using remote cloud computing infrastructure. Some or alltests may be dynamically modified by the system if required. Forexample, the system may compute the periphery of vision for a user andmove the stimulus to that computed periphery. The results may bepresented on the apparatus, sent to a third party (like the testadministrator) using any electronic means, or displayed on a mobileapplication.

Tests that may be automatically performed by the apparatus may includethe following, as well as other tests which seek to elicit eye movementwhich can be correlated with impairment.

Calibration—This test seeks to precisely calibrate the system to theface shape, eye characteristics and eye geometry of the user. The eyemovement of the user is measured during a multi-part test that mayinclude measuring interpupillary distance, eye tracking using a movingor stationary stimulus, the measurement of pupil size, blink rate, andother biometric or eye movement characteristics. Utilizing this data,the system may make adjustments to the software or hardware of thetesting apparatus to precisely align the eye tracking sensors, eyecameras, and other biometric sensors to capture optimal data. Thecalibration test may additionally measure reaction time, and capture eyemovement data during the calibration test for later evaluation by themachine learning/artificial intelligence algorithm. In some cases, auser's facial geometry may fall outside of the parameters that aretestable by the apparatus. In these cases, the system may notify theuser and/or the test administrator or other interested parties.

Lack of Smooth Pursuit—This test evaluates a user's ability to track astimulus smoothly with the eyes only. The system conducts this test bymoving the stimulus smoothly to left and right, and/or up and down oneor more times. An impaired person's eyes may exhibit saccades, orjerking, in the motion of their eyes.

Horizontal Gaze Nystagmus—tests for involuntary jerking movement of theeyes at the left and right periphery of vision. The system conducts thistest by moving the stimulus to the left and/or right periphery of theuser's vision one or more times. An impaired person's eyes may exhibitnystagmus, or sustained jerking motion, in the eyes during stationaryfocus.

Onset of Horizontal Gaze Nystagmus Before 45 Degrees—tests forinvoluntary jerking of the eye using a stimulus held at 45 degrees orless horizontally from center. Similar to the Horizontal Gaze Nystagmustest above, the system moves the stimulus left and/or right one or moretimes. However, this test stops the stimulus at or before 45 degreesfrom center. The test may also stop the stimulus as soon as nystagmus isdetected and record the angle of onset. An impaired person's eyes mayexhibit nystagmus at angles of onset that are not on the periphery ofvision.

Vertical Gaze Nystagmus—tests for involuntary jerking of the eye at theupper or lower periphery of vision. The system conducts this test bymoving the stimulus up and/or down to the upper and/or lower peripheryof the user's vision one or more times. The system may also stop thestimulus as soon as nystagmus is detected and record the angle of onset.An impaired person's eyes may exhibit nystagmus on the upper or lowerperipheries of vision, or at an angle of onset prior to the periphery.

Lack of Convergence—tests for the ability to converge the eyes and/orhold them in that position. The system moves a stimulus in a circle,which may be 12″ in diameter, or another size, then moves the stimulusslowly toward the bridge of the nose. The stimulus may stopapproximately 2″ from the bridge of the nose, or another distance. Thesystem may also move the stimulus directly toward the bridge of thenose, omitting the aforementioned circle, and may repeat this test oneor more times. This test should cause a user's eyes to cross (convergein focus). An impaired person's eyes may exhibit a lack of ability toconverge the focus of the eyes (lack of convergence) or a lack ofability to hold the eyes in a converged position, or other abnormalcharacteristics.

Pupillary Rebound Dilation—tests the involuntary pupil reflex responseto changing light conditions. The system may optionally remove thestimulus or replace it with another focus, such as a timer. The systemmay display a variety of light levels, including but not limited to roomlight, full dark, and bright light. In the room light condition, thesystem may simulate the amount of light in a normally lit room. In thefull dark light condition, the system may remove all or most ambientlight from the display. This “blacked out” state persists until theuser's eyes are fully dilated in response—this may be 90 seconds, oranother period of time. The system may then increase the light level tobright light to test how the pupils respond to light. The system maychange light levels for both eyes simultaneously or individually. Thesystem measures pupil size throughout this test and may optionallymonitor eye movement. The rate at which a user's pupil responds to achange in light conditions may indicate impairment. For example, aperson's pupils may persist in a dilated or constricted state, dependingon the substance a person is impaired on. Further, the pupils maydisplay pupillary unrest or hippus, in which the pupils may not stopadjusting size, despite steady light conditions. This presents as aconstricting and dilation of the pupils, and it may be eithersignificant or very subtle.

Reaction Speed—this test seeks to quantify a user's reaction time as areduction or increase in reaction time is indicative of impairment oncertain substances. This test may be derived from reaction times inresponse to changes in the previously mentioned tests, or it may beperformed through the appearance or movement of a stimulus in a randomlygenerated quadrant of the testing apparatus display. The systemgenerates a location at which to display the stimulus using a randomnumber generator. Each number is assigned to a location on which thesystem may display the stimulus. Once the random number generator hasdetermined a location for the stimulus, the system displays the stimulusand measures the time required for the user's eyes to land on thestimulus. The test may use a stimulus that moves to the next location ordisappears and reappears in the next location. The test may be repeatedmultiple times to determine a representative reaction time for the user.

All of the tests detailed above may be conducted with the stimuluspositioned as needed, though a virtualized distance of 12″-15″ from theuser's eyes, unless otherwise noted, has been utilized thus far. Thesize of the stimulus can be variable or fixed as required. The headshould not move during these tests, and the testing apparatus may detectsuch movement and provide user and/or administrator with appropriatefeedback, as well as automatically pause, or restart the tests asrequired.

The system may additionally utilize programmatic, statistical or machinelearning/artificial intelligence methods to determine if a user isfollowing test instructions during the testing process. A non-compliantuser may, for example, simply close one or both of their eyes for someor all of the testing process. Other types of non-compliance caninclude, but are not limited to, looking straight ahead or at anotherrandom point for the duration or part of the testing process, moving theeyes in a random manner for some or all of the testing process, trackingthe stimulus only intermittently, ignoring test instructions, rapidlyblinking for a sustained period of time, or cessation of stimulustracking at any point. The detection of non-compliant users and thesubsequent notification of non-compliance to test administrators orother interested parties is an important step in accurately determiningimpairment.

When complete, the tests may be uploaded to a cloud server forprocessing or processed on the device or a mobile application. Theresulting test data may be compressed prior to transmitting to any otherdevice using either lossless, or lossy compression methods. Knowncompression methods may be utilized in this step.

Either supervised or unsupervised machine learning techniques may beused to create accurate impairment detection models. Initial dataprocessing may include data normalization and cleaning. This willsimplify and format the data to eliminate unnecessary timesteps,simplify 3-dimensional rotations, and split each resulting series ofdata into logical components according to the targeted movementpatterns. A time-series statistical model may be utilized to determineinitial fit of the data against known characteristics of impairment.This may be followed by deep learning evaluative techniques.Specifically, clustering with Fourier Transforms, and ROCKET algorithmsmay be utilized to establish a baseline. These algorithms both automatethe process of feature engineering, and they provide complimentaryvisual interpretations. This baseline can then be measured against twoarchitectures: Dilated Convolutional Neural Networks, and Deep Learningbased attention models.

Time Series Analysis—Baseline Clustering Fourier Transform: For datawith a periodic nature, the entire dataset may be described by a linearcombination of wave functions, allowing the definition of its locationin a new coordinate system. Having the recordings mapped in this newsystem can allow them to be compared, clustered, and visualizedspatially. Algorithms such as k-nearest neighbors, basic multi-layerperceptrons, convolutional neural networks, random forest classifierbased on catch22 features, Diverse Representation Canonical IntervalForest Classifier, TSFresh classifier, or WEASEL may be applied toclassify this time series data into behaviors and impairment levels.

Time Series Analysis—Classification with ROCKET Algorithm: Rather thanusing different waves as features, ROCKET uses a set of wavelets, finitesegments of waves. The ROCKET algorithm starts by generatingapproximately 10,000 wavelets of random shapes, sizes, and dilations.The weight of each wavelet feature in a recording is how prevalent thepattern of that wavelet is along the recording. If the pattern shows upmany times in the recording, the value for that feature will be higherthan if the pattern appears seldom or not at all. This may be used tofurther classify characteristics of impairment and sobriety. In theROCKET algorithm these features are then used as the variables in alinear classifier, such as ridge regression classification.

Deep Learning Analysis—Classification with Dilated Convolutional NeuralNetwork: Deep learning methods have higher performance (and bettergeneralization) than classifying with the two previously discussedbaselines. A deep learning model called WaveNet using convolutionalneural network architecture that uses dilated convolution layers toscale to larger sequences in a memory-efficient way may be utilized.While typically used for forecasting, it can be adapted forclassification. Like the kernel size of a typical convolutional layer,the dilation size in the dilated convolutional layers can be adjusted tofit the needs of the data. In this case, the dilation size may be setsuch that the kernel of the outermost layer covers a full targetmovement and tracking event.

Deep Learning Analysis—Classification with Transformers—Deep Learning:Transformers may be applied to a sequence of eye movements in much thesame way that transformers are applied to sequences of words in naturallanguage processing. Like the other approaches, this algorithm finds aset of common features to describe eye movements and transforms eachinto this feature space. The resulting recordings become a set ofmatrices with fixed width equal to the number of features used, andvarying length equal to the number of target movements in thatrecording. The transformer applies attention heads that are able toidentify informative movements by looking at relationships with othermovements. This allows the algorithm to find and model larger scalepatterns than other algorithms. Aside from these heads, the rest of thealgorithm may be either a multi-layer perceptron or a convolutionalneural network.

The system may also utilize these statistical/machine learningtechniques to determine a profile for abnormal eye movement that doesnot correlate with either impairment or normal sober eye movement. Thisabnormal eye movement may be a sign of impairment on an unknownsubstance or combination of substances, a sign of a head injury or otherneurological abnormality, or another reason. Classification of a testsubject's eye movement as abnormal, but not associated with knownsubstances of impairment, and the communication of this to the testadministrator, or other third party, may therefore be valuableinformation.

Other equipment that may be used in the process includes an optionalexternal or adjunct device running a software application, which can beused by a test subject or administrator to communicate with the testingapparatus. This companion software application may be utilized by testadministrators, or test subjects to enter additional information thatthe test apparatus cannot automatically detect. For example, demographicdata, personally identifiable information, or other notes may beentered. This application can also be used to perform processing of thedata from tests if required. Lastly, this application may be used todisplay results or other feedback on the test, such as upload status orother information.

This companion software application may include several key userinterface functionalities, namely: a button used to start and/or stopthe test, an indication of the test progress, and an indication of testresults once the test data has been evaluated. Optional otherfunctionality may include a live view from the cameras of the apparatus,a live animation of the view from the cameras, the ability to enterdemographic data, display data upload status, the ability to enterindividual identifiers like an employee ID number, the ability to watchrecorded test video, the ability to enter suspected or known substancesof impairment, and the ability to enter notes or testing rationale.

If a subject is found to be impaired or with abnormal eye movement dueto one or multiple tests, the test results may be communicatedautomatically to the test administrator, or other third party asrequired. This is accomplished through any of the following methods:email, SMS/MMS messages, notification utilizing the companion softwareapplication, or any other digital means. The data may be utilized inevaluated form, or in raw form by test administrators, test subjects, orother interested parties as required.

If a subject is found to be not impaired, the test data may similarly bemade available, or stored for later reference. Test subjects couldalternatively request that test data be deleted rather than stored.

An output of the apparatus may be video recording and/or digital imagerywhich is not evaluated, used to train evaluative algorithms, or alteredby the system. The video recording and/or imagery is captured by one ormore cameras and stored either on the testing apparatus, on a mobile ordesktop computing platform, or in cloud computing environments. Thisvideo and/or imagery can then be evaluated by existing human DrugRecognition Experts or other interested parties as required. Eithervisible or infrared cameras may be utilized to capture video and imageryfrom the automatically performed tests. The eye tracking video recordingmay be displayed in raw format, or optional test data such as gazedirection, pupil size, or stimulus may be overlayed on the video ordisplayed in conjunction with the video to provide additional importantcontextual information.

FIG. 1 is a flow chart of an embodiment of a method 100 to determinewhether a test subject is unimpaired, impaired, and/or the substancethat has impaired the test subject. The method 100 includes using atesting apparatus, at step 105. Optionally, the method 100 may includetesting subject compliance monitoring, at step 110. For example, a usermay not be in compliance by simply closing one or both of their eyes forsome or all of the testing process. Additional types of non-compliancecan include, but are not limited to, looking straight ahead or atanother random point for the duration or part of the testing process,moving the eyes in a random manner for some or all of the testingprocess, tracking the stimulus only intermittently, ignoring testinstructions, rapidly blinking for a sustained period of time, orcessation of stimulus tracking at any point. The detection ofnon-compliant users may be important step in accurately determiningwhether or not an individual is impaired.

The method 100 includes collecting data from the testing apparatus, atstep 115. For example, that data collected may include, but is notlimited to, eye movement video 120, pupil size data 125, gaze vectordata 130, or the like. The data collected may include other data 135,such as other biometric data from a user. The method 100 includesstoring data, at step 140. Optionally, the method 100 may includepre-processing the data, at step 145. The method 100 include processingthe data with an automated impairment decision engine, at step 150.

The method 100 may include the automated impairment decision enginesystem using machine learning models, at step 155, to detect active(real-time) impairment from cannabis and other drugs, fatigue, or otherconditions through the utilization of gaze vector data, pupil size data,and optionally other biometric data. The method 100 may include theautomated impairment decision engine system using statistical models, atstep 160, to detect active (real-time) impairment from cannabis andother drugs, fatigue, or other conditions through the utilization ofgaze vector data, pupil size data, and optionally other biometric data.The method 100 includes matching a substance, at step 165. The matchingof a substance may be based on machine learning models and/orstatistical models. The method 100 includes reporting no impairment tothe test administrator, at step 170. For example, if the automatedimpairment decision engine determines that a test subject is notimpaired, the test administrator is informed of this decision. Themethod 100 includes reporting impairment and/or substance to the testadministrator, at step 175. For example, if the automated impairmentdecision engine determines that a test subject is impaired, the testadministrator is informed of this decision. Likewise, the automatedimpairment decision engine may inform the test administrator thesubstance which impaired the test subject.

FIG. 2 shows a schematic of an embodiment of a system 200 of the presentdisclosure. The system includes a test administrator application 255connected, wired or wirelessly, to a controller 250. The controller 250may be integral with the test administrator application 255 as would beappreciated by one of ordinary skill in the art having the benefit ofthis disclosure. The test administrator application 255 and controller250 are connected to a display 225. The display 225 may be but is notlimited to a monitor, video display, a virtual reality headset,television, monitor, kiosk-mounted display, augmented reality glasses, aholographic display, or the like. The connections of the system 200 maybe wired or wireless as would be appreciated by one of ordinary skill inthe art having the benefit this disclosure. The system 200 includes astimulus 230 that is shown on the display 225. The stimulus 230 may bemoved about the display 225 by the controller 250 and/or the testadministrator application 255 as discussed herein.

The system 200 includes one or more eye tracking sensors 235. The one ormore eye tracking sensors 235 monitor the eyes of a test subject duringtesting as discussed herein. The system 200 may include a camera 245.The camera 245 may be focused on the eye(s) of a test subject duringtesting. The system 200 may include one or more other sensors 240 asdiscussed herein. The data captured by the one or more eye trackingsensors 235, the camera 245, and/or the one or more other sensors 240may be stored in onboard storage 215 and/or cloud storage 220 as wouldbe appreciated by one of ordinary skill in the art having the benefit ofthis disclosure.

The system 200 includes a processor 210 and an automated impairmentdecision engine 205. The processor 210 may be integral to the automatedimpairment decision engine 205 as would be appreciated by one ofordinary skill in the art having the benefit of this disclosure. Theprocessor 210 is configured to process the data from the one or more eyetracking sensors 235, the camera 245, and/or the one or more othersensors 240. The data may be received from the onboard storage 215and/or the cloud storage 220. Alternatively, the data may be receiveddirectly from the one or more eye tracking sensors 235, the camera 245,and/or the one or more other sensors 240 as would be appreciated by oneof ordinary skill in the art having the benefit of this disclosure. Theautomated impairment decision engine 205 uses the processed data todetermine whether a test subject is impaired and/or the substance uponwhich a test subject is impaired as discussed herein.

FIG. 3 is a flow chart of an embodiment of a method of training a systemor automated impairment decision engine to determine whether anindividual is impaired or unimpaired. The method 300 includes capturingdata from participants in both impaired states and unimpaired states, atstep 310. The method 300 includes capturing both unimpaired user data315 and impaired user data 320. Prior to capturing data, the method 300may include screening participants, at step 305.

The method 300 includes applying captured data to unsupervised machinelearning, at step 330. The method 300 includes applying captured data tosupervised machine learning, at step 340. The method 300 includesapplying captured data to statistical analysis, at step 350. The method300 includes implementing applied captured data to an automatedimpairment decision engine, at step 360. One of ordinary skill in theart having the benefit of this disclosure would recognize that theapplied captured data from unsupervised machine learning, the appliedcaptured data from supervised machine learning, the applied captureddata from statistical analysis, or the like may be used to train anautomated impairment decision engine. Alternatively, applied captureddata from all three or a combination may be used to train an automatedimpairment decision engine to determine whether a test subject isunimpaired, impaired, and/or the substance that has impaired the testsubject as discussed herein.

The described embodiments are susceptible to various modifications andalternative forms, and specific examples thereof have been shown by wayof example in the drawings and are herein described in detail. It shouldbe understood, however, that the described embodiments are not to belimited to the particular forms or methods disclosed, but to thecontrary, the present disclosure is to cover all modifications,equivalents, and alternatives. Additionally, elements of a givenembodiment should not be construed to be applicable to only that exampleembodiment and therefore elements of one example embodiment can beapplicable to other embodiments. Additionally, in some embodiments,elements that are specifically shown in some embodiments can beexplicitly absent from further embodiments. Accordingly, the recitationof an element being present in one example should be construed tosupport some embodiments where such an element is explicitly absent.

What is claimed is:
 1. A system comprising: a display; a stimulus on thedisplay; a controller, wherein the controller is programmed to move thestimulus about the display; one or more sensors, wherein the one or moresensors track eye movements and pupil size of a user due to movement ofthe stimulus or light conditions; and a processor programmed to analyzethe eye movements and pupil size data.
 2. The system of claim 1, whereinthe controller is programmed to move the stimulus to perform animpairment test and the processor is programmed to determine andevaluate impairment based on data from the one or more sensors.
 3. Thesystem of claim 1, wherein the controller is programmed to stimulatepupil response using varying light conditions to perform an impairmenttest and the processor is programmed to determine and evaluateimpairment based on data from the one or more sensors.
 4. The system ofclaim 1, wherein the one or more sensor capture pupil size data andwherein the processor is programmed to analyze the pupil size data toevaluate impairment.
 5. The system of claim 1, wherein the one or moresensors capture gaze vector data of the user's vision and wherein theprocessor is programmed to analyze the captured gaze vector data toevaluate impairment.
 6. The system of claim 5, wherein the displayfurther comprises a virtual reality headset, television, monitor,kiosk-mounted display, augmented reality glasses, or a holographicdisplay.
 7. The system of claim 5, wherein the processor is programmedto utilize statistical models, machine learning, artificial intelligencealgorithms, or a combination of these to analyze the eye movements orother biometric data.
 8. The system of claim 7, wherein the controlleris programmed to precisely calibrate the system to a face shape, eyecharacteristics, and eye geometry of the user.
 9. The system of claim 8,wherein the controller is programmed to move the stimulus smoothly tothe left and right or up and down.
 10. The system of claim 9, whereinthe controller is programmed to move the stimulus one or more times to ahorizontal periphery of the user's vision.
 11. The system of claim 10,wherein the controller is programmed to move the stimulus left or rightand stop the movement at 45 degrees from center.
 12. The system of claim11, wherein the controller is programmed to move the stimulus to avertical periphery of the user's vision.
 13. The system of claim 12,wherein the controller is programmed to move the stimulus in a circle orto stimulate convergence in focus by bringing the stimulus toward thesubject's nose.
 14. The system of claim 13, wherein the controller isprogrammed to display specific light levels and measure pupillary reflexresponse.
 15. The system of claim 14, wherein the controller isprogrammed to capture data using skin-contact or non-contact sensors,such as temperature, heart rate, heart rate variability, respiratoryrate, pulse oxygenation, heart rhythm, blood pressure, and muscle tone.16. The system of claim 15, wherein the controller is programmed toperform chemical analysis on the sweat excreted by the test subject. 17.The system of claim 16, wherein the controller is programmed to measurelack of convergence, saccades, nystagmus, hippus, eye smoothness,reaction time, pupillary rebound dilation, and pupillary reflex for thepurposes of impairment detection.
 18. A method comprising: using atesting apparatus; collecting data from the testing apparatus; storingthe collected data; and processing the data with an automated impairmentdecision engine to determine whether a test subject is impaired.
 19. Themethod of claim 18, further comprising using machine learning models orstatistical analysis to determine whether a test subject is impaired.20. A method comprising: capturing data from participants in both animpaired state and an unimpaired state; applying captured data tounsupervised machine learning; applying captured data to supervisedmachine learning; applying captured data to statistical analysis;implementing the applied captured data to an automated impairmentdecision engine to train the automated impairment decision engine tolearn to determine whether a test subject is impaired or unimpaired.